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Clustering method based on convolution

A convolution and clustering technology, applied in the field of clustering, can solve problems such as complex methods and lower clustering efficiency, and achieve the effect of improving algorithm complexity

Active Publication Date: 2020-02-21
XIAN UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] The existing clustering method uses the comparison method for comparison. This method is relatively complicated,

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  • Clustering method based on convolution
  • Clustering method based on convolution

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Embodiment 1

[0028] In this implementation of the algorithm, under the CoMP system model, to improve the system gain as the goal to eliminate the interference between cells, we redefine x(n) and h(n), where x(n) is the generated user interference matrix, h (n) is the appropriate convolution kernel for us to deal with the interference selection, so that the convolution kernel acts on the original interference data matrix in turn, and the output results are sorted by bit-by-bit multiplication and addition, and a group of outputs with the best clustering effect , the specific steps are somewhat similar to the convolutional layer in the convolutional neural network, such as figure 1 The matrix on the left is the generated random user interference matrix. Since the user's interference to itself is zero and the interference value has nothing to do with the order of users, the matrix is ​​a symmetric matrix with zero diagonal elements.

[0029] like figure 2 As shown, first import the generated...

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Abstract

The invention relates to a clustering method based on convolution. The method comprises the following specific steps: 1, importing an interference matrix: importing a user interference matrix into theinterference matrix; 2, setting a convolution kernel, and performing convolution operation processing on the first row of the interference matrix by using the convolution kernel; 3, sorting the convolution results according to a sequence from small to large; 4, dividing the group of users with the minimum result into a cluster; 5, deleting the clustered users from the interference matrix, whereinthe interference matrix is reduced; and 6, executing the step 1 to the step 4 for the new interference matrix until no user can delete the new interference matrix. The method is based on convolutioncharacteristics. The convolution algorithm is used for an interference matrix, final clustering is achieved with low calculation complexity, the convolution clustering algorithm greatly improves the performance of system edge users, system average users and system center users compared with a comparison algorithm, and the algorithm complexity of convolution operation clustering is not high.

Description

technical field [0001] The invention relates to the technical field of clustering, in particular to a convolution-based clustering method. Background technique [0002] Ultra-dense networking is to shorten the distance between stations and greatly increase the site density through more intensive wireless network deployment, thereby improving spectrum reuse rate, user experience rate and network capacity per unit area. However, ultra-dense networking will generate more cell-edge users, and the performance of these cell-edge users will be severely degraded. Appropriate cooperative clusters can reduce the interference between cells, and will not bring more computational complexity and energy consumption of signal processing in ultra-dense networks, but it can avoid serious interference between users and achieve the purpose of improving system capacity. So clustering becomes very important. Convolution is an important mathematical method, which reflects the overlap area betwee...

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Application Information

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IPC IPC(8): H04W40/32H04W72/08G06N3/04
CPCH04W40/32G06N3/045H04W72/541
Inventor 梁彦霞王军选刘欣姜静孙长印何华杜剑波周继军王颖
Owner XIAN UNIV OF POSTS & TELECOMM
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